When the term neuroscience was originally forged, it referred mostly to research at the molecular and cellular levels. Higher-order cognition was not deemed amenable to rigorous research and was thus relegated to the domain of “soft sciences.” But the second half of the 20th century witnessed nothing short of a revolution as the old taboos were broken and the boundaries of neuroscience research drastically expanded. This revolution provided the foundation for a further expansion of neuroscience research in the 21st century and propelled into existence a whole array of studies of normal and abnormal higher-order brain functions. These developments are reflected in the emergence of terminology which would have been dismissed as oxymoronic even a few decades ago.
Here’s a guide to the new neuroscience lexicon, and where it comes from.
Cognitive neuroscience is a discipline concerned with the brain mechanisms of higher-order mental processes such as decision making, memory, attention, language, and more.
In the past, the effects of brain damage provided the only window into these complex functions. This amounted to a somewhat circuitous path of discovery: Scientists tried to unravel the mysteries of a normal brain by studying abnormal brains. Examining the effects of brain lesions as a method of understanding the fundamental principles of normal brain organization remains an important part of the neuroscience research tool kit, but no longer the only one. Powerful tools of functional neuroimaging have become available over the last few decades. They include functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and other techniques that visually reveal which parts of the brain are active during which cognitive processes in a healthy, active brain.
Cognitive neuroscience is a generic term subsuming a range of specific research areas, some of which will be reviewed below.
It used to be said that medicine is more of an art than a science — particularly referring to the clinical disciplines dealing with the brain and the mind. It was more of an indictment than a compliment, reflecting the paucity of scientific foundation.
In the past decades, psychiatry especially was virtually disconnected from rigorous science. By the second half of the 20th century, this began to change, as our understanding of the brain and brain disorders was expanding by leaps and bounds, and as the various disorders that used to be regarded in the past as “diseases of the soul” were increasingly understood as brain diseases.
Today, the detail of understanding of conditions such as schizophrenia, depression, obsessive-compulsive disorder, and other psychiatric disorders rivals that of major internal-medicine diseases. The branch of psychiatry concerned with the biological bases of such disorders, and with the use of this knowledge for their diagnoses and treatment, is often referred to as neuropsychiatry. But the clinical ramifications of neurobiological breakthroughs are not limited to psychiatric disorders. Their impact extends from various conditions associated with development, like attention deficit hyperactivity disorder (ADHD), to conditions associated with aging, like Alzheimer’s disease and other dementias.
Collectively, the clinical applications of brain research are often referred to as clinical neuroscience. In the past there would have been little justification for this term, since the diagnosis and treatment of these conditions was by and large devoid of scientific foundation, but now such a foundation exists and is getting more robust and solid by the year.
Many historians of science believe that a discipline reaches a state of maturity only with the development of a theoretical arm, which relies on mathematical and computational methods to examine complex processes using simplified models. A dynamic relationship between theoretical and experimental arms of a discipline then develops, the former helping generate predictions, and the latter testing them. This is precisely the relationship between modern theoretical and experimental physics, but the foundations of theoretical physics can be traced back to the invention of calculus and differential equations by Newton and Leibnitz in the 17th century.
By this measure, neuroscience today is barely where physics was in the beginning of the 17th century — which is not entirely surprising, considering a much greater complexity of living organisms (the central nervous system in particular) compared to inorganic matter. But over the last few decades we have witnessed the birth of computational neuroscience, a theoretical arm of brain research.
At its disposal, computational neuroscience has the tools that could not even have been imagined by 17th-century physicists. Powerful computers allow one to create dynamic models of complex processes which may be too complex for the analytical tools of traditional mathematics. These models permit an entirely new type of research — a hybrid of theoretical and experimental approaches in the form of a computer program. The program models a complex neurobiological process, and can then be run under varying conditions emulating real-life circumstances. This “virtual experiment” is conducted with a model of a biological object, rather than with the biological object itself.
Neuroeconomics is an area where cognitive neuroscience intersects with economics. Until recently, economic theories were constructed as dispassionate, rational, “objective” mathematical models. But these models completely ignored the fact that in real life, economic decisions are made by human beings, not by machines, and the assumption of “perfect rationality” failed to reflect the human factor, with all its emotions, biases, and preconceptions. (consider the United States’ recent economic woes).
To account for the human factor, a field of behavioral economics arose, and the psychologist Daniel Kahneman received a Nobel prize in economics for his pioneering work on the human irrationality in economic decision-making. Behavioral economics eventually gave rise to neuroeconomics, concerned with the brain mechanisms of complex economic decision-making in environments characterized by uncertainty and fluidity.